Examples with TipStatesModel dr.evomodel.tipstatesmodel.TipStatesModel used on opensource projects

Example 1 with TipStatesModel

use of dr.evomodel.tipstatesmodel.TipStatesModel in project beast-mcmc by beast-dev.

the class BalancedBeagleTreeLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    boolean useAmbiguities = xo.getAttribute(BeagleTreeLikelihoodParser.USE_AMBIGUITIES, false);
    /*int instanceCount = xo.getAttribute(INSTANCE_COUNT, 1);
        if (instanceCount < 1) {
            instanceCount = 1;
        }

        String ic = System.getProperty(BEAGLE_INSTANCE_COUNT);
        if (ic != null && ic.length() > 0) {
            instanceCount = Integer.parseInt(ic);
        }*/
    PatternList patternList = (PatternList) xo.getChild(PatternList.class);
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    GammaSiteRateModel siteRateModel = (GammaSiteRateModel) xo.getChild(GammaSiteRateModel.class);
    FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
    BranchModel branchModel = (BranchModel) xo.getChild(BranchModel.class);
    if (branchModel == null) {
        SubstitutionModel substitutionModel = (SubstitutionModel) xo.getChild(SubstitutionModel.class);
        if (substitutionModel == null) {
            substitutionModel = siteRateModel.getSubstitutionModel();
        }
        if (substitutionModel == null) {
            throw new XMLParseException("No substitution model available for TreeLikelihood: " + xo.getId());
        }
        branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
    }
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    // if (xo.getChild(TipStatesModel.class) != null) {
    // throw new XMLParseException("Sequence Error Models are not supported under BEAGLE yet. Please use Native BEAST Likelihood.");
    // }
    PartialsRescalingScheme scalingScheme = PartialsRescalingScheme.DEFAULT;
    if (xo.hasAttribute(BeagleTreeLikelihoodParser.SCALING_SCHEME)) {
        // scalingScheme = PartialsRescalingScheme.parseFromString(xo.getStringAttribute(BeagleTreeLikelihoodParser.SCALING_SCHEME));
        if (scalingScheme == null)
            throw new XMLParseException("Unknown scaling scheme '" + xo.getStringAttribute(BeagleTreeLikelihoodParser.SCALING_SCHEME) + "' in " + "OldBeagleTreeLikelihood object '" + xo.getId());
    }
    boolean delayScaling = true;
    Map<Set<String>, Parameter> partialsRestrictions = null;
    if (xo.hasChildNamed(PARTIALS_RESTRICTION)) {
        XMLObject cxo = xo.getChild(PARTIALS_RESTRICTION);
        TaxonList taxonList = (TaxonList) cxo.getChild(TaxonList.class);
        // Parameter parameter = (Parameter) cxo.getChild(Parameter.class);
        try {
            TreeUtils.getLeavesForTaxa(treeModel, taxonList);
        } catch (TreeUtils.MissingTaxonException e) {
            throw new XMLParseException("Unable to parse taxon list: " + e.getMessage());
        }
        throw new XMLParseException("Restricting internal nodes is not yet implemented.  Contact Marc");
    }
    /*if (instanceCount == 1 || patternList.getPatternCount() < instanceCount) {
            return createTreeLikelihood(
                    patternList,
                    treeModel,
                    branchModel,
                    siteRateModel,
                    branchRateModel,
                    tipStatesModel,
                    useAmbiguities,
                    scalingScheme,
                    partialsRestrictions,
                    xo
            );
        }*/
    // first run a test for instanceCount == 1
    System.err.println("\nTesting instanceCount == 1");
    Likelihood baseLikelihood = createTreeLikelihood(patternList, treeModel, branchModel, siteRateModel, branchRateModel, tipStatesModel, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
    double start = System.nanoTime();
    for (int i = 0; i < TEST_RUNS; i++) {
        baseLikelihood.makeDirty();
        baseLikelihood.getLogLikelihood();
    }
    double end = System.nanoTime();
    double baseResult = end - start;
    System.err.println("Evaluation took: " + baseResult);
    if (!(patternList instanceof SitePatterns)) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with BEAUti-selected codon partitioning.");
    }
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    // List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    List<Likelihood> likelihoods = null;
    CompoundLikelihood compound = null;
    int instanceCount = 2;
    boolean optimal = false;
    while (optimal == false) {
        System.err.println("\nCreating instanceCount == " + instanceCount);
        likelihoods = new ArrayList<Likelihood>();
        for (int i = 0; i < instanceCount; i++) {
            Patterns subPatterns = new Patterns((SitePatterns) patternList, 0, 0, 1, i, instanceCount);
            AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModel, branchModel, siteRateModel, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
            treeLikelihood.setId(xo.getId() + "_" + instanceCount);
            likelihoods.add(treeLikelihood);
        }
        // construct compoundLikelihood
        compound = new CompoundLikelihood(instanceCount, likelihoods);
        // test timings
        System.err.println("\nTesting instanceCount == " + instanceCount);
        start = System.nanoTime();
        for (int i = 0; i < TEST_RUNS; i++) {
            compound.makeDirty();
            compound.getLogLikelihood();
        }
        end = System.nanoTime();
        double newResult = end - start;
        System.err.println("Evaluation took: " + newResult);
        if (baseResult / newResult > TEST_CUTOFF) {
            instanceCount++;
            baseResult = newResult;
        } else {
            optimal = true;
            instanceCount--;
            System.err.println("\nCreating final BeagleTreeLikelihood with instanceCount: " + instanceCount);
            likelihoods = new ArrayList<Likelihood>();
            for (int i = 0; i < instanceCount; i++) {
                Patterns subPatterns = new Patterns((SitePatterns) patternList, 0, 0, 1, i, instanceCount);
                AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModel, branchModel, siteRateModel, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
                treeLikelihood.setId(xo.getId() + "_" + instanceCount);
                likelihoods.add(treeLikelihood);
            }
            // construct compoundLikelihood
            compound = new CompoundLikelihood(instanceCount, likelihoods);
        }
    }
    return compound;
/*for (int i = 0; i < instanceCount; i++) {

            Patterns subPatterns = new Patterns((SitePatterns)patternList, 0, 0, 1, i, instanceCount);

            AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(
                    subPatterns,
                    treeModel,
                    branchModel,
                    siteRateModel,
                    branchRateModel,
                    null,
                    useAmbiguities,
                    scalingScheme,
                    partialsRestrictions,
                    xo);
            treeLikelihood.setId(xo.getId() + "_" + instanceCount);
            likelihoods.add(treeLikelihood);
        }

        return new CompoundLikelihood(likelihoods);*/
}

Example 2 with TipStatesModel

use of dr.evomodel.tipstatesmodel.TipStatesModel in project beast-mcmc by beast-dev.

the class BeagleTreeLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    boolean useAmbiguities = xo.getAttribute(USE_AMBIGUITIES, false);
    int instanceCount = xo.getAttribute(INSTANCE_COUNT, 1);
    if (instanceCount < 1) {
        instanceCount = 1;
    }
    String ic = System.getProperty(BEAGLE_INSTANCE_COUNT);
    if (ic != null && ic.length() > 0) {
        instanceCount = Integer.parseInt(ic);
    }
    PatternList patternList = (PatternList) xo.getChild(PatternList.class);
    MutableTreeModel treeModel = (MutableTreeModel) xo.getChild(MutableTreeModel.class);
    GammaSiteRateModel siteRateModel = (GammaSiteRateModel) xo.getChild(GammaSiteRateModel.class);
    FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
    BranchModel branchModel = (BranchModel) xo.getChild(BranchModel.class);
    if (branchModel == null) {
        SubstitutionModel substitutionModel = (SubstitutionModel) xo.getChild(SubstitutionModel.class);
        if (substitutionModel == null) {
            substitutionModel = siteRateModel.getSubstitutionModel();
        }
        if (substitutionModel == null) {
            throw new XMLParseException("No substitution model available for TreeLikelihood: " + xo.getId());
        }
        branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
    }
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    if (branchModel instanceof EpochBranchModel && rootFreqModel != null) {
        EpochBranchModel epochBranchModel = (EpochBranchModel) branchModel;
        epochBranchModel.setRootFrequencyModel(rootFreqModel);
    }
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    // if (xo.getChild(TipStatesModel.class) != null) {
    // throw new XMLParseException("Sequence Error Models are not supported under BEAGLE yet. Please use Native BEAST Likelihood.");
    // }
    PartialsRescalingScheme scalingScheme = PartialsRescalingScheme.DEFAULT;
    boolean delayScaling = true;
    if (xo.hasAttribute(SCALING_SCHEME)) {
        scalingScheme = PartialsRescalingScheme.parseFromString(xo.getStringAttribute(SCALING_SCHEME));
        if (scalingScheme == null)
            throw new XMLParseException("Unknown scaling scheme '" + xo.getStringAttribute(SCALING_SCHEME) + "' in " + "OldBeagleTreeLikelihood object '" + xo.getId());
    }
    if (xo.hasAttribute(DELAY_SCALING)) {
        delayScaling = xo.getBooleanAttribute(DELAY_SCALING);
    }
    Map<Set<String>, Parameter> partialsRestrictions = null;
    if (xo.hasChildNamed(PARTIALS_RESTRICTION)) {
        XMLObject cxo = xo.getChild(PARTIALS_RESTRICTION);
        TaxonList taxonList = (TaxonList) cxo.getChild(TaxonList.class);
        // Parameter parameter = (Parameter) cxo.getChild(Parameter.class);
        try {
            TreeUtils.getLeavesForTaxa(treeModel, taxonList);
        } catch (TreeUtils.MissingTaxonException e) {
            throw new XMLParseException("Unable to parse taxon list: " + e.getMessage());
        }
        throw new XMLParseException("Restricting internal nodes is not yet implemented.  Contact Marc");
    }
    int beagleThreadCount = -1;
    if (System.getProperty(BEAGLE_THREAD_COUNT) != null) {
        beagleThreadCount = Integer.parseInt(System.getProperty(BEAGLE_THREAD_COUNT));
    }
    if (beagleThreadCount == -1) {
        // the default is -1 threads (automatic thread pool size) but an XML attribute can override it
        int threadCount = xo.getAttribute(THREADS, -1);
        if (System.getProperty(THREAD_COUNT) != null) {
            threadCount = Integer.parseInt(System.getProperty(THREAD_COUNT));
        }
        // Todo: allow for different number of threads per beagle instance according to pattern counts
        if (threadCount >= 0) {
            System.setProperty(BEAGLE_THREAD_COUNT, Integer.toString(threadCount / instanceCount));
        }
    }
    if (instanceCount == 1 || patternList.getPatternCount() < instanceCount) {
        return createTreeLikelihood(patternList, treeModel, branchModel, siteRateModel, branchRateModel, tipStatesModel, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
    }
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    for (int i = 0; i < instanceCount; i++) {
        Patterns subPatterns = new Patterns(patternList, i, instanceCount);
        AbstractTreeLikelihood treeLikelihood = createTreeLikelihood(subPatterns, treeModel, branchModel, siteRateModel, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, partialsRestrictions, xo);
        treeLikelihood.setId(xo.getId() + "_" + instanceCount);
        likelihoods.add(treeLikelihood);
    }
    return new CompoundLikelihood(likelihoods);
}

Example 3 with TipStatesModel

use of dr.evomodel.tipstatesmodel.TipStatesModel in project beast-mcmc by beast-dev.

the class TreeDataLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    boolean useAmbiguities = xo.getAttribute(USE_AMBIGUITIES, false);
    boolean usePreOrder = xo.getAttribute(USE_PREORDER, false);
    boolean branchRateDerivative = xo.getAttribute(BRANCHRATE_DERIVATIVE, usePreOrder);
    boolean branchInfinitesimalDerivative = xo.getAttribute(BRANCHINFINITESIMAL_DERIVATIVE, false);
    if (usePreOrder != (branchRateDerivative || branchInfinitesimalDerivative)) {
        throw new RuntimeException("Need to specify derivative types.");
    }
    PreOrderSettings settings = new PreOrderSettings(usePreOrder, branchRateDerivative, branchInfinitesimalDerivative);
    // TreeDataLikelihood doesn't currently support Instances defined from the command line
    // int instanceCount = xo.getAttribute(INSTANCE_COUNT, 1);
    // if (instanceCount < 1) {
    // instanceCount = 1;
    // }
    // 
    // String ic = System.getProperty(BEAGLE_INSTANCE_COUNT);
    // if (ic != null && ic.length() > 0) {
    // instanceCount = Integer.parseInt(ic);
    // }
    List<PatternList> patternLists = new ArrayList<PatternList>();
    List<SiteRateModel> siteRateModels = new ArrayList<SiteRateModel>();
    List<BranchModel> branchModels = new ArrayList<BranchModel>();
    boolean hasSinglePartition = false;
    PatternList patternList = (PatternList) xo.getChild(PatternList.class);
    if (patternList != null) {
        hasSinglePartition = true;
        patternLists.add(patternList);
        GammaSiteRateModel siteRateModel = (GammaSiteRateModel) xo.getChild(GammaSiteRateModel.class);
        siteRateModels.add(siteRateModel);
        FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
        BranchModel branchModel = (BranchModel) xo.getChild(BranchModel.class);
        if (branchModel == null) {
            SubstitutionModel substitutionModel = (SubstitutionModel) xo.getChild(SubstitutionModel.class);
            if (substitutionModel == null) {
                substitutionModel = siteRateModel.getSubstitutionModel();
            }
            if (substitutionModel == null) {
                throw new XMLParseException("No substitution model available for partition in DataTreeLikelihood: " + xo.getId());
            }
            branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
        }
        branchModels.add(branchModel);
    }
    int k = 0;
    for (int i = 0; i < xo.getChildCount(); i++) {
        if (xo.getChildName(i).equals(PARTITION)) {
            if (hasSinglePartition) {
                throw new XMLParseException("Either a single set of patterns should be given or multiple 'partitions' elements within DataTreeLikelihood: " + xo.getId());
            }
            k += 1;
            XMLObject cxo = (XMLObject) xo.getChild(i);
            patternList = (PatternList) cxo.getChild(PatternList.class);
            patternLists.add(patternList);
            GammaSiteRateModel siteRateModel = (GammaSiteRateModel) cxo.getChild(GammaSiteRateModel.class);
            siteRateModels.add(siteRateModel);
            FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
            BranchModel branchModel = (BranchModel) cxo.getChild(BranchModel.class);
            if (branchModel == null) {
                SubstitutionModel substitutionModel = (SubstitutionModel) xo.getChild(SubstitutionModel.class);
                if (substitutionModel == null) {
                    substitutionModel = siteRateModel.getSubstitutionModel();
                }
                if (substitutionModel == null) {
                    throw new XMLParseException("No substitution model available for partition " + k + " in DataTreeLikelihood: " + xo.getId());
                }
                branchModel = new HomogeneousBranchModel(substitutionModel, rootFreqModel);
            }
            branchModels.add(branchModel);
            BranchRateModel branchRateModel = (BranchRateModel) cxo.getChild(BranchRateModel.class);
            if (branchRateModel != null) {
                throw new XMLParseException("Partitions are not currently allowed their own BranchRateModel in TreeDataLikelihood object '" + xo.getId());
            }
        }
    }
    if (patternLists.size() == 0) {
        throw new XMLParseException("Either a single set of patterns should be given or multiple 'partitions' elements within DataTreeLikelihood: " + xo.getId());
    }
    Tree treeModel = (Tree) xo.getChild(Tree.class);
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    if (branchRateModel == null) {
        throw new XMLParseException("BranchRateModel missing from TreeDataLikelihood object '" + xo.getId());
    // branchRateModel = new DefaultBranchRateModel();
    }
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    final boolean preferGPU = xo.getAttribute(PREFER_GPU, false);
    PartialsRescalingScheme scalingScheme = PartialsRescalingScheme.DEFAULT;
    if (xo.hasAttribute(SCALING_SCHEME)) {
        scalingScheme = PartialsRescalingScheme.parseFromString(xo.getStringAttribute(SCALING_SCHEME));
        if (scalingScheme == null)
            throw new XMLParseException("Unknown scaling scheme '" + xo.getStringAttribute(SCALING_SCHEME) + "' in " + "TreeDataLikelihood object '" + xo.getId());
    }
    final boolean delayScaling = xo.getAttribute(DELAY_SCALING, true);
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    return createTreeDataLikelihood(patternLists, branchModels, siteRateModels, treeModel, branchRateModel, null, useAmbiguities, preferGPU, scalingScheme, delayScaling, settings);
}

Example 4 with TipStatesModel

use of dr.evomodel.tipstatesmodel.TipStatesModel in project beast-mcmc by beast-dev.

the class MultiPartitionDataLikelihoodParser method parseXMLObject.

public Object parseXMLObject(XMLObject xo) throws XMLParseException {
    boolean useAmbiguities = xo.getAttribute(USE_AMBIGUITIES, false);
    int instanceCount = xo.getAttribute(INSTANCE_COUNT, 1);
    if (instanceCount < 1) {
        instanceCount = 1;
    }
    String ic = System.getProperty(BEAGLE_INSTANCE_COUNT);
    if (ic != null && ic.length() > 0) {
        instanceCount = Integer.parseInt(ic);
    }
    if (DEBUG) {
        System.out.println("instanceCount: " + instanceCount);
    }
    List<PatternList> patternLists = xo.getAllChildren(PatternList.class);
    TreeModel treeModel = (TreeModel) xo.getChild(TreeModel.class);
    List<SiteRateModel> siteRateModels = xo.getAllChildren(SiteRateModel.class);
    FrequencyModel rootFreqModel = (FrequencyModel) xo.getChild(FrequencyModel.class);
    List<BranchModel> branchModels = xo.getAllChildren(BranchModel.class);
    if (branchModels.size() == 0) {
        if (DEBUG) {
            System.out.println("branchModels == null");
        }
        branchModels = new ArrayList<BranchModel>();
        List<SubstitutionModel> substitutionModels = xo.getAllChildren(SubstitutionModel.class);
        if (substitutionModels.size() == 0) {
            if (DEBUG) {
                System.out.println("substitutionModels == null");
            }
            for (SiteRateModel siteRateModel : siteRateModels) {
                SubstitutionModel substitutionModel = ((GammaSiteRateModel) siteRateModel).getSubstitutionModel();
                if (substitutionModel == null) {
                    throw new XMLParseException("No substitution model available for TreeDataLikelihood: " + xo.getId());
                }
                branchModels.add(new HomogeneousBranchModel(substitutionModel, rootFreqModel));
            }
        }
        if (DEBUG) {
            System.out.println("branchModels size: " + branchModels.size());
        }
        for (BranchModel branchModel : branchModels) {
            System.out.println("  " + branchModel.getId() + "  " + branchModel.getModelName());
        }
    }
    BranchRateModel branchRateModel = (BranchRateModel) xo.getChild(BranchRateModel.class);
    if (branchRateModel == null) {
        branchRateModel = new DefaultBranchRateModel();
    }
    if (DEBUG) {
        System.out.println("BranchRateModel: " + branchRateModel.getId());
    }
    TipStatesModel tipStatesModel = (TipStatesModel) xo.getChild(TipStatesModel.class);
    PartialsRescalingScheme scalingScheme = PartialsRescalingScheme.DEFAULT;
    boolean delayScaling = true;
    if (xo.hasAttribute(SCALING_SCHEME)) {
        scalingScheme = PartialsRescalingScheme.parseFromString(xo.getStringAttribute(SCALING_SCHEME));
        if (scalingScheme == null)
            throw new XMLParseException("Unknown scaling scheme '" + xo.getStringAttribute(SCALING_SCHEME) + "' in " + "OldBeagleTreeLikelihood object '" + xo.getId());
    }
    if (xo.hasAttribute(DELAY_SCALING)) {
        delayScaling = xo.getBooleanAttribute(DELAY_SCALING);
    }
    if (instanceCount == 1) {
        if (DEBUG) {
            System.out.println("instanceCount == 1");
        }
        return createTreeDataLikelihood(patternLists, treeModel, branchModels, siteRateModels, branchRateModel, tipStatesModel, useAmbiguities, scalingScheme, delayScaling, xo);
    }
    if (tipStatesModel != null) {
        throw new XMLParseException("BEAGLE_INSTANCES option cannot be used with a TipStateModel (i.e., a sequence error model).");
    }
    List<PatternList> patternInstanceLists = new ArrayList<PatternList>();
    for (int j = 0; j < patternLists.size(); j++) {
        for (int i = 0; i < instanceCount; i++) {
            patternInstanceLists.add(new Patterns(patternLists.get(j), i, instanceCount));
        }
    }
    return createTreeDataLikelihood(patternLists, treeModel, branchModels, siteRateModels, branchRateModel, null, useAmbiguities, scalingScheme, delayScaling, xo);
}

Example 5 with TipStatesModel

use of dr.evomodel.tipstatesmodel.TipStatesModel in project beast-mcmc by beast-dev.

the class PMDTestProblem method testPMD.

public void testPMD() throws Exception {
    Parameter popSize = new Parameter.Default(ConstantPopulationModelParser.POPULATION_SIZE, 496432.69917113904, 0, Double.POSITIVE_INFINITY);
    ConstantPopulationModel constantModel = createRandomInitialTree(popSize);
    TreeIntervals intervalList = new TreeIntervals(treeModel, null, null);
    CoalescentLikelihood coalescent = new CoalescentLikelihood(intervalList, constantModel);
    coalescent.setId("coalescent");
    // clock model
    Parameter rateParameter = new Parameter.Default(StrictClockBranchRates.RATE, 4.0E-7, 0, 100.0);
    StrictClockBranchRates branchRateModel = new StrictClockBranchRates(rateParameter);
    // Sub model
    Parameter freqs = new Parameter.Default(new double[] { 0.25, 0.25, 0.25, 0.25 });
    Parameter kappa = new Parameter.Default(HKYParser.KAPPA, 1.0, 1.0E-8, Double.POSITIVE_INFINITY);
    FrequencyModel f = new FrequencyModel(Nucleotides.INSTANCE, freqs);
    HKY hky = new HKY(kappa, f);
    // siteModel
    GammaSiteModel siteModel = new GammaSiteModel(hky);
    Parameter mu = new Parameter.Default(GammaSiteModelParser.MUTATION_RATE, 1.0, 0, Double.POSITIVE_INFINITY);
    siteModel.setMutationRateParameter(mu);
    // SequenceErrorModel
    Parameter ageRelatedRateParameter = new Parameter.Default(SequenceErrorModelParser.AGE_RELATED_RATE, 4.0E-7, 0, 100.0);
    TipStatesModel aDNADamageModel = new SequenceErrorModel(null, null, SequenceErrorModel.ErrorType.TRANSITIONS_ONLY, null, ageRelatedRateParameter, null);
    // treeLikelihood
    SitePatterns patterns = new SitePatterns(alignment, null, 0, -1, 1, true);
    TreeLikelihood treeLikelihood = new TreeLikelihood(patterns, treeModel, siteModel, branchRateModel, aDNADamageModel, false, false, true, false, false);
    treeLikelihood.setId(TreeLikelihoodParser.TREE_LIKELIHOOD);
    // Operators
    OperatorSchedule schedule = new SimpleOperatorSchedule();
    MCMCOperator operator = new ScaleOperator(kappa, 0.75);
    operator.setWeight(1.0);
    schedule.addOperator(operator);
    operator = new ScaleOperator(rateParameter, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter allInternalHeights = ((DefaultTreeModel) treeModel).createNodeHeightsParameter(true, true, false);
    operator = new UpDownOperator(new Scalable[] { new Scalable.Default(rateParameter) }, new Scalable[] { new Scalable.Default(allInternalHeights) }, 0.75, 3.0, AdaptationMode.ADAPTATION_ON);
    schedule.addOperator(operator);
    operator = new ScaleOperator(popSize, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    operator = new ScaleOperator(ageRelatedRateParameter, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter rootHeight = ((DefaultTreeModel) treeModel).getRootHeightParameter();
    rootHeight.setId(TREE_HEIGHT);
    operator = new ScaleOperator(rootHeight, 0.75);
    operator.setWeight(3.0);
    schedule.addOperator(operator);
    Parameter internalHeights = ((DefaultTreeModel) treeModel).createNodeHeightsParameter(false, true, false);
    operator = new UniformOperator(internalHeights, 30.0);
    schedule.addOperator(operator);
    operator = new SubtreeSlideOperator(((DefaultTreeModel) treeModel), 15.0, 49643.2699171139, true, false, false, false, AdaptationMode.ADAPTATION_ON, AdaptableMCMCOperator.DEFAULT_ADAPTATION_TARGET);
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.NARROW, treeModel, 15.0);
    // operator.doOperation();
    schedule.addOperator(operator);
    operator = new ExchangeOperator(ExchangeOperator.WIDE, treeModel, 3.0);
    // operator.doOperation();
    schedule.addOperator(operator);
    operator = new WilsonBalding(treeModel, 3.0);
    // operator.doOperation();
    schedule.addOperator(operator);
    // ??? correct?
    operator = new DeltaExchangeOperator(freqs, new int[] { 1, 1, 1, 1 }, 0.01, 1.0, false, AdaptationMode.ADAPTATION_ON);
    schedule.addOperator(operator);
    // CompoundLikelihood
    OneOnXPrior likelihood1 = new OneOnXPrior();
    likelihood1.addData(popSize);
    OneOnXPrior likelihood2 = new OneOnXPrior();
    likelihood2.addData(kappa);
    List<Likelihood> likelihoods = new ArrayList<Likelihood>();
    likelihoods.add(likelihood1);
    likelihoods.add(likelihood2);
    likelihoods.add(coalescent);
    Likelihood prior = new CompoundLikelihood(0, likelihoods);
    prior.setId(CompoundLikelihoodParser.PRIOR);
    likelihoods.clear();
    likelihoods.add(treeLikelihood);
    Likelihood likelihood = new CompoundLikelihood(-1, likelihoods);
    likelihoods.clear();
    likelihoods.add(prior);
    likelihoods.add(likelihood);
    Likelihood posterior = new CompoundLikelihood(0, likelihoods);
    posterior.setId(CompoundLikelihoodParser.POSTERIOR);
    // Log
    ArrayLogFormatter formatter = new ArrayLogFormatter(false);
    MCLogger[] loggers = new MCLogger[2];
    loggers[0] = new MCLogger(formatter, 1000, false);
    loggers[0].add(posterior);
    loggers[0].add(treeLikelihood);
    loggers[0].add(rootHeight);
    loggers[0].add(rateParameter);
    loggers[0].add(ageRelatedRateParameter);
    loggers[0].add(popSize);
    loggers[0].add(kappa);
    loggers[0].add(coalescent);
    loggers[1] = new MCLogger(new TabDelimitedFormatter(System.out), 10000, false);
    loggers[1].add(posterior);
    loggers[1].add(treeLikelihood);
    loggers[1].add(rootHeight);
    loggers[1].add(rateParameter);
    // MCMC
    MCMC mcmc = new MCMC("mcmc1");
    MCMCOptions options = new MCMCOptions(1000000);
    mcmc.setShowOperatorAnalysis(true);
    mcmc.init(options, posterior, schedule, loggers);
    mcmc.run();
    // time
    System.out.println(mcmc.getTimer().toString());
    // Tracer
    List<Trace> traces = formatter.getTraces();
    ArrayTraceList traceList = new ArrayTraceList("PMDTest", traces, 0);
    for (int i = 1; i < traces.size(); i++) {
        traceList.analyseTrace(i);
    }
    // <expectation name="clock.rate" value="1.5E-7"/>
    // <expectation name="errorModel.ageRate" value="0.7E-7"/>
    // <expectation name="hky.kappa" value="10"/>
    TraceCorrelation kappaStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(HKYParser.KAPPA));
    assertExpectation(HKYParser.KAPPA, kappaStats, 10);
    TraceCorrelation rateStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(StrictClockBranchRates.RATE));
    assertExpectation(StrictClockBranchRates.RATE, rateStats, 1.5E-7);
    TraceCorrelation ageRateStats = traceList.getCorrelationStatistics(traceList.getTraceIndex(SequenceErrorModelParser.AGE_RELATED_RATE));
    assertExpectation(SequenceErrorModelParser.AGE_RELATED_RATE, ageRateStats, 0.7E-7);
}